analysis_Theron_08262021
Theron Palmer
8/26/2021
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## anyMissing, rowMediansThe TCGA MAF summary file
maf_file <- "/media/theron/My_Passport/TCGA_junctions/maf_summary.txt"
mc3_maf = read.table(maf_file,header=T)
mc3_maf$Tumor_Sample_ID <- vapply(TCGAbarcode(mc3_maf$Tumor_Sample_Barcode,sample=T),
function(val){substr(val,1,nchar(val)-1)},
character(1))
rownames(mc3_maf) <- mc3_maf$Tumor_Sample_Barcode
mc3_maf$participant_ID <- TCGAbarcode(mc3_maf$Tumor_Sample_Barcode,participant=T)mut_sig_perc <- readRDS("/media/theron/My_Passport/TCGA_junctions/TCGA_cancers/mut_sig_percentages.rds")
mut_sig_perc$sample_ID<-vapply(TCGAbarcode(rownames(mut_sig_perc),sample=T),function(sample){
substr(sample,1,nchar(sample)-1)
},character(1))
apobec <- c("T[C>T]A","T[C>T]T","T[C>G]A","T[C>G]T")Accumulating mutation data per sample only annotated
junc summary mean
TCGA_cibersort_all <- read.table("/media/theron/My_Passport/TCGA_junctions/ext_dat/TCGA.Kallisto.fullIDs.cibersort.relative.tsv",header=T)
TCGA_cibersort_all$SampleID <- str_replace_all(TCGA_cibersort_all$SampleID,"[.]","-")
cibersort_cells <- c("participant_ID","B.cells.naive","B.cells.memory","Plasma.cells",
"T.cells.CD8","T.cells.CD4.naive","T.cells.CD4.memory.resting",
"T.cells.CD4.memory.activated","T.cells.follicular.helper",
"T.cells.regulatory..Tregs.","T.cells.gamma.delta","NK.cells.resting",
"NK.cells.activated","Monocytes","Macrophages.M0","Macrophages.M1",
"Macrophages.M2","Dendritic.cells.resting","Dendritic.cells.activated",
"Mast.cells.resting","Mast.cells.activated","Eosinophils","Neutrophils")
junc_rse_file <- "/media/theron/My_Passport/TCGA_junctions/TCGA_cancers/CHOL/juncrse.rds"
junc_rse <- readRDS(junc_rse_file)
junc_metadata <- as.data.frame(junc_rse@colData@listData)
junc_rse_cols <- colnames(junc_metadata)
tumor_data_file <- "/media/theron/My_Passport/TCGA_junctions/TCGA_cancers/filenames.txt"
tumor_data <- read.table(tumor_data_file)
cancers <- basename(tumor_data$V1)
# TMB<-list()
cluster_metrics_tum <- data.frame(cancers)
TMB_all <- data.frame(cancers)
TMB_all$av_cor <- NA
TMB_all$av_pval <- NA
TMB_all$med_cor <- NA
TMB_all$med_pval <- NA
TMB_all$max_cor <- NA
TMB_all$max_pval <- NA
rownames(TMB_all) <- cancers
all_genes <- c()
# cols_to_look_for <- c("tcga.gdc_cases.diagnoses.tumor_stage",
# "tcga.gdc_cases.diagnoses.days_to_death",
# "tcga.cgc_case_primary_therapy_outcome_success",
# "tcga.cgc_case_pathologic_stage")
stage_1 <- c("Stage I","Stage IA","Stage IB")
stage_2 <- c("Stage I","Stage IIA","Stage IIB","Stage IIC")
stage_3 <- c("Stage III", "Stage IIIA", "Stage IIIB", "Stage IIIC")
stage_4 <- c("Stage IV","Stage IVA", "Stage IVB","Stage IVC")
for (i in seq(nrow(tumor_data))){
tumor_dir <- tumor_data[i,]
cancer <- basename(tumor_dir)
print(sprintf("%s: %d out of %d",cancer,i,nrow(tumor_data)))
# tumor_meta_file <- sprintf("%s/%s_metadata.txt",tumor_dir,cancer)
# tumor_meta <- read.table(tumor_meta_file,quote="",sep="\t")
tumor_meta_file <- sprintf("%s/%s_metadata.rds",tumor_dir,cancer)
tumor_meta <- readRDS(tumor_meta_file)
tumor_meta$participant_ID <- vapply(TCGAbarcode(tumor_meta[,4],participant=T,sample=T),function(samp){substr(samp,1,nchar(samp)-1)},character(1))
TCGA_cibersort_all$participant_ID <- vapply(TCGAbarcode(TCGA_cibersort_all$SampleID,participant=T,sample=1),
function(samp){substr(samp,1,nchar(samp)-1)},character(1))
tumor_meta$nbases<-tumor_meta[,ncol(tumor_meta)-9]
mc3_maf_small<-subset(mc3_maf,Tumor_Sample_ID %in% tumor_meta$participant_ID)
TCGA_cibersort_small<-subset(TCGA_cibersort_all,participant_ID %in% tumor_meta$participant_ID)
mc3_maf_small <- mc3_maf_small[complete.cases(mc3_maf_small),]
mc3_maf_small$type <- vapply(rownames(mc3_maf_small),function(barcode){
type<-as.numeric(substr(strsplit(barcode,"-")[[1]][4],1,2))
if (type <= 9){
return("T")
} else if (type > 9 & type <= 19){
return ("N")
} else {
return ("C")
}
},character(1))
mc3_maf_small$TMB<-log10(mc3_maf_small$total+1)
mc3_maf_small <- mc3_maf_small %>% dplyr::filter(type == "T")
tumor_geno_file <- sprintf("%s/%s_genotypes.txt",tumor_dir,cancer)
tumor_geno <- read.table(tumor_geno_file,header=T)
tumor_geno <- tumor_geno %>% dplyr::filter(type=="T")
tumor_geno <- tumor_geno[complete.cases(tumor_geno),]
external_ids <- tumor_geno$external_id
splice_mut_file <- sprintf("%s/filenames.txt",tumor_dir)
splice_mut_files <- read.table(splice_mut_file)
for (j in seq(length(splice_mut_files$V1))){
if (j == 1){
combined_gene_metric <- readRDS(splice_mut_files$V1[j])
} else {
combined_gene_metric <- cbind(combined_gene_metric,readRDS(splice_mut_files$V1[j]))
}
}
combined_gene_metric[combined_gene_metric==-Inf]<-0
combined_gene_metric[is.na(combined_gene_metric)]<-0
combined_gene_metric <- combined_gene_metric[,external_ids]
all_genes <- unique(c(all_genes,rownames(combined_gene_metric)))
colnames(combined_gene_metric) <- vapply(colnames(combined_gene_metric),function(col_name){
col_name<-str_replace(col_name,"X","")
col_name <- str_replace_all(col_name,"[.]","-")
tumor_geno$sample_id[which(tumor_geno$external_id == col_name)[1]]
},character(1))
mc3_maf_small <- mc3_maf_small %>% dplyr::filter(Tumor_Sample_ID %in% colnames(combined_gene_metric))
TCGA_cibersort_small <- TCGA_cibersort_all %>% dplyr::filter(participant_ID %in% colnames(combined_gene_metric))
combined_gene_metric<-combined_gene_metric[,mc3_maf_small$Tumor_Sample_ID]
combined_gene_metric_per_sample<-data.frame(colnames(combined_gene_metric))
combined_gene_metric_per_sample$av <- vapply(seq(ncol(combined_gene_metric)),
function(col_val){mean(as.numeric(combined_gene_metric[,col_val]),na.rm=T)},
numeric(1))
combined_gene_metric_per_sample$med <- vapply(seq(ncol(combined_gene_metric)),
function(col_val){median(as.numeric(combined_gene_metric[,col_val]),na.rm=T)},
numeric(1))
combined_gene_metric_per_sample$max <- vapply(seq(ncol(combined_gene_metric)),
function(col_val){max(as.numeric(combined_gene_metric[,col_val]),na.rm=T)},
numeric(1))
combined_gene_metric_per_sample$sum <- vapply(seq(ncol(combined_gene_metric)),
function(col_val){sum(as.numeric(combined_gene_metric[,col_val]),na.rm=T)},
numeric(1))
combined_gene_metric_per_sample$TMB <- mc3_maf_small$TMB
combined_gene_metric_per_sample$cancer <- cancer
colnames(combined_gene_metric_per_sample) <- c("sample","gene_metric_av","gene_metric_med",
"gene_metric_max","gene_metric_sum","TMB","cancer")
cibersort_per_samp <- lapply(combined_gene_metric_per_sample$sample,
function(samp){
if (samp %in% TCGA_cibersort_small$participant_ID){
return(TCGA_cibersort_small[which(TCGA_cibersort_small$participant_ID==samp),cibersort_cells,drop=F])
} else {
a<-data.frame(t(c(samp,rep(NA,length(cibersort_cells)))))
colnames(a)<-cibersort_cells
return(a)
}
})
cibersort_per_samp_df<-cibersort_per_samp[[1]]
for (k in seq(2,length(cibersort_per_samp))){
cibersort_per_samp_df <- rbind(cibersort_per_samp_df,cibersort_per_samp[[k]])
}
cibersort_per_samp_df[,c("gene_metric_av","TMB","cancer")]<-t(vapply(cibersort_per_samp_df$participant_ID,function(ID){
as.character(combined_gene_metric_per_sample[combined_gene_metric_per_sample$sample==ID,c("gene_metric_av","TMB","cancer")])
},character(3)))
cibersort_per_samp_df$gene_metric_av <- as.numeric(cibersort_per_samp_df$gene_metric_av)
cibersort_per_samp_df$TMB <- as.numeric(cibersort_per_samp_df$TMB)
combined_gene_metric_perc <- data.frame(t(vapply(combined_gene_metric_per_sample$sample,function(samp){
a<-which(mut_sig_perc$sample_ID == samp)
if (length(a)==0){
return(rep(-1,ncol(mut_sig_perc)-1))
} else {
return(as.numeric(mut_sig_perc[a,seq(96)]))
}
},numeric(ncol(mut_sig_perc)-1))))
colnames(combined_gene_metric_perc)<-colnames(mut_sig_perc)[seq(96)]
if (i == 1){
combined_gene_metric_per_sample_all <- combined_gene_metric_per_sample
combined_gene_metric_perc_all <- combined_gene_metric_perc
} else {
combined_gene_metric_per_sample_all <- rbind(combined_gene_metric_per_sample_all,combined_gene_metric_per_sample)
combined_gene_metric_perc_all <- rbind( combined_gene_metric_perc_all, combined_gene_metric_perc)
}
# combined_gene_metric_log10<-as.matrix(log10(combined_gene_metric+1))
# colnames(combined_gene_metric_log10)<-colnames(combined_gene_metric)
#
# print(Heatmap(combined_gene_metric_log10,
# top_annotation = HeatmapAnnotation(TMB=anno_barplot(mc3_maf_small$TMB)),
# show_row_names=F,
# show_column_names = F,
# cluster_rows=T,
# cluster_columns=T))
a<-cor.test(combined_gene_metric_per_sample$gene_metric_av,combined_gene_metric_per_sample$TMB,method="pearson")
TMB_all[cancer,"av_cor"]<-a$estimate
TMB_all[cancer,"av_pval"]<-a$p.value
a<-cor.test(combined_gene_metric_per_sample$gene_metric_med,combined_gene_metric_per_sample$TMB,method="pearson")
TMB_all[cancer,"med_cor"]<-a$estimate
TMB_all[cancer,"med_pval"]<-a$p.value
a<-cor.test(combined_gene_metric_per_sample$gene_metric_max,combined_gene_metric_per_sample$TMB,method="pearson")
TMB_all[cancer,"max_cor"]<-a$estimate
TMB_all[cancer,"max_pval"]<-a$p.value
combined_gene_metric_perc_cor_pval <- data.frame(t(vapply(seq(ncol(mut_sig_perc)-1),function(col_val){
ret_val <- c()
mut_col <- as.numeric(combined_gene_metric_perc[,col_val])
aa<-data.frame(combined_gene_metric_per_sample$gene_metric_av,mut_col)
colnames(aa)<-c("gene_metric_av","mut_col")
aa<-aa %>% dplyr::filter(mut_col >= 0)
a<-cor.test(aa$gene_metric_av,aa$mut_col,method="pearson")
ret_val <- c(ret_val,a$estimate,a$p.value)
aa<-data.frame(combined_gene_metric_per_sample$gene_metric_med,mut_col)
colnames(aa)<-c("gene_metric_med","mut_col")
aa<-aa %>% dplyr::filter(mut_col >= 0)
a<-cor.test(aa$gene_metric_med,aa$mut_col,method="pearson")
ret_val <- c(ret_val,a$estimate,a$p.value)
aa<-data.frame(combined_gene_metric_per_sample$gene_metric_max,mut_col)
colnames(aa)<-c("gene_metric_max","mut_col")
aa<-aa %>% dplyr::filter(mut_col >= 0)
a<-cor.test(aa$gene_metric_max,aa$mut_col,method="pearson")
ret_val <- c(ret_val,a$estimate,a$p.value)
},numeric(6))))
colnames(combined_gene_metric_perc_cor_pval)<-c("av_cor","av_pval","med_cor","med_pval","max_cor","max_pval")
rownames(combined_gene_metric_perc_cor_pval) <- colnames(combined_gene_metric_perc)
print(ggplot(combined_gene_metric_per_sample,aes(x=gene_metric_av,y=TMB))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top",)+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
if (i == 1){
cibersort_per_samp_df_all <- cibersort_per_samp_df
} else {
cibersort_per_samp_df_all <- rbind(cibersort_per_samp_df_all,cibersort_per_samp_df)
}
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=B.cells.naive))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=B.cells.memory))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Plasma.cells))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=T.cells.CD8))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=T.cells.CD4.naive))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=T.cells.CD4.memory.resting))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=T.cells.CD4.memory.activated))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=T.cells.follicular.helper))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=T.cells.regulatory..Tregs.))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=T.cells.gamma.delta))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=NK.cells.resting))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=NK.cells.activated))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Monocytes))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Macrophages.M0))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Macrophages.M1))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Macrophages.M2))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Dendritic.cells.resting))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Dendritic.cells.activated))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Mast.cells.resting))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Mast.cells.activated))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Eosinophils))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Neutrophils))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm")+
labs(title=sprintf(cancer)))
# print(ggplot(combined_gene_metric_per_sample,aes(x=log10(gene_metric_med+1),y=TMB))+
# geom_point()+
# stat_cor(method = "pearson")+
# geom_smooth(method="lm")+
# labs(title=sprintf(cancer)))
#
# print(ggplot(combined_gene_metric_per_sample,aes(x=log10(gene_metric_max+1),y=TMB))+
# geom_point()+
# stat_cor(method = "pearson")+
# geom_smooth(method="lm")+
# labs(title=sprintf(cancer)))
combined_gene_metric_perc_cor_pval <- combined_gene_metric_perc_cor_pval %>% dplyr::filter(av_pval <= 0.05 | med_pval <= 0.05 | max_pval <= 0.05)
rownames(combined_gene_metric_perc_cor_pval) <- vapply(rownames(combined_gene_metric_perc_cor_pval),function(rname){
if (rname %in% apobec){
return(sprintf("%s:abobec",rname))
} else {
return(rname)
}
},character(1))
combined_gene_metric_perc_cor <- combined_gene_metric_perc_cor_pval[,c("av_cor","med_cor","max_cor")]
combined_gene_metric_perc_pval <- combined_gene_metric_perc_cor_pval[,c("av_pval","med_pval","max_pval")]
if (nrow(combined_gene_metric_perc_cor_pval) > 0){
print(Heatmap(as.matrix(combined_gene_metric_perc_cor), cell_fun = function(j, i, x, y, w, h, fill) {
if(combined_gene_metric_perc_pval[i, j] < 0.005) {
grid.text("**", x, y)
} else if(combined_gene_metric_perc_pval[i, j] < 0.05) {
grid.text("*", x, y)
}
},name=sprintf("%s: splicemut vs mutation type",cancer),cluster_columns=F))
} else {
print(sprintf("%s: no significant mutation trends",cancer))
}
}## [1] "BLCA: 1 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "BRCA: 2 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "CHOL: 3 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "COAD: 4 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "HNSC: 5 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "KICH: 6 out of 14"## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "KIRP: 7 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "LIHC: 8 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "LUAD: 9 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "LUSC: 10 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "PRAD: 11 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "READ: 12 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "THCA: 13 out of 14"## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## [1] "UCEC: 14 out of 14"## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Correlation Datatable
datatable(TMB_all, caption = "TMB")TMB Correlation for all samples
ggplot(combined_gene_metric_per_sample_all,aes(x=gene_metric_av,y=TMB))+
geom_point()+
stat_cor(method = "pearson",label.x.npc="middle",label.y.npc="top")+
geom_smooth(method="lm")+
labs("All Samples")## `geom_smooth()` using formula 'y ~ x'
Cell Proportions all samples
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=B.cells.naive))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=B.cells.memory))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Plasma.cells))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=T.cells.CD8))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=T.cells.CD4.naive))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=T.cells.CD4.memory.resting))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=T.cells.CD4.memory.activated))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=T.cells.follicular.helper))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=T.cells.regulatory..Tregs.))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=T.cells.gamma.delta))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=NK.cells.resting))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=NK.cells.activated))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Monocytes))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df,aes(x=gene_metric_av,y=Macrophages.M0))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Macrophages.M1))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Macrophages.M2))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Dendritic.cells.resting))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Dendritic.cells.activated))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Mast.cells.resting))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Mast.cells.activated))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Eosinophils))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Neutrophils))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'
print(ggplot(cibersort_per_samp_df_all,aes(x=gene_metric_av,y=Neutrophils))+
geom_point()+
stat_cor(method = "pearson",
label.x.npc = "left",
label.y.npc = "top")+
geom_smooth(method="lm"))## `geom_smooth()` using formula 'y ~ x'